Generation of audience appropriate content

ABSTRACT

Multimedia content to be played on a multimedia player device can be received. Whether the multimedia content contains audience-inappropriate content can be determined. Replacement content corresponding to the audience-inappropriate content can be generated. The generated replacement content can be caused to play on the multimedia player device in lieu of the audience-inappropriate content.

BACKGROUND

The present application relates generally to computers and computerapplications, and more particularly to automatic detection andgeneration of audience appropriate content.

Content ratings (e.g., movie rating, video games rating) provide a basicguideline about the multi-media content, but sometimes such generalrating may still not provide appropriate content to appropriateaudience. For example, guardians or parents may want to have controlover content (e.g., video games).

BRIEF SUMMARY

A computer-implemented method, in one aspect, can include receivingmultimedia content to be played on a multimedia player device. Themethod can also include determining that the multimedia content containsaudience-inappropriate content. The method can further includegenerating replacement content corresponding to theaudience-inappropriate content. The method can also include causing thegenerated replacement content to play on the multimedia player device inlieu of the audience-inappropriate content.

A system can include a hardware processor and a memory device coupledwith the hardware processor. The hardware processor can be configured toreceive multimedia content to be played on a multimedia player device.The hardware processor can also be configured to determine that themultimedia content contains audience-inappropriate content. The hardwareprocessor can also be configured to generate replacement contentcorresponding to the audience-inappropriate content. The hardwareprocessor can also be configured to cause the generated replacementcontent to play on the multimedia player device in lieu of theaudience-inappropriate content.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an overview of components of a systemin an embodiment.

FIG. 2 is a flow diagram illustrating a method in an embodiment.

FIG. 3 is a diagram showing components of a system in one embodimentthat can generate audience-appropriate content.

FIG. 4 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment.

DETAILED DESCRIPTION

In embodiments, a system, method and technique are provided whichgenerates an alternative audience appropriate multi-media segment inreal time. The system, method and technique may use generative modelsand deep learning technology to generate such content. For example, agenerative adversarial can be implemented to create alternative audienceappropriate media segment or segments to replace sensitive content (ordetected inappropriate content).

FIG. 1 is a diagram illustrating an overview of components of a systemin an embodiment. The components shown include computer-implementedcomponents, for instance, implemented and/or run on one or moreprocessors or hardware processors, or coupled with one or more hardwareprocessors. One or more hardware processors, for example, may includecomponents such as programmable logic devices, microcontrollers, memorydevices, and/or other hardware components, which may be configured toperform respective tasks described in the present disclosure. Coupledmemory devices may be configured to selectively store instructionsexecutable by one or more hardware processors.

A processor may be a central processing unit (CPU), a graphicsprocessing unit (GPU), a field programmable gate array (FPGA), anapplication specific integrated circuit (ASIC), another suitableprocessing component or device, or one or more combinations thereof. Theprocessor may be coupled with a memory device. The memory device mayinclude random access memory (RAM), read-only memory (ROM) or anothermemory device, and may store data and/or processor instructions forimplementing various functionalities associated with the methods and/orsystems described herein. The processor may execute computerinstructions stored in the memory or received from another computerdevice or medium.

In an embodiment, audience-appropriate multimedia content can begenerated in real-time to alter sensitive contents of a multi-mediapiece (e.g., movies, video games). The system, in an embodiment, flagsas sensitive, inappropriate segments of the content, for example, takinginto account the targeted audience. The system may, before a sensitivesegment is played, perform an analysis of the audience to identify thelevel of sensitivity of the content for that given audience. The systemregenerates an alternative content for that segment that is appropriatefor the given audience and plays or presents that generated contentinstead. The system may implement generative models to regenerate suchalternative content for that segment. For example, if the script of thesegment is flagged as inappropriate (through an analysis of captiondata, a speech to text on audio, video or image), a new script and/ornew video can be generated for that segment to replace the sensitivecontent. In an aspect, a visual and audio content of that segment can berecreated also to synchronize with the recreated script.

At 102, a number of profiles can be created for the audience. Theprofiles can be generic such as “adult”, “kindergartner”, “pre-teen”,“family”, or custom defined. Profiles can be defined on a play device,or associated with a play device.

Multimedia content 116 is received. For example, multimedia content,which is triggered to be played on a device, can be intercepted. In anembodiment, the multimedia content can be handled as a whole or bysegments. For instance, based on one or more factors such as the size ofthe multimedia content, availability of memory, speed of hardware,and/or another factor, the multimedia content can be processed as awhole by segments. For example, the multimedia content can be segmentedinto segments and the segments processed. While the followingdescription refers to a “segment”, a “segment” can be the wholemultimedia content, if the multimedia content is being processed in thewhole (e.g., based on one or more above factors). Either method can beused for implementation to emulate real time experience. At 104, theaudio script and/or imagery of a segment of a multimedia content 116 areextracted. For instance, captions, if any, in the segment can beextracted. In addition, for instance, the natural language processingtechniques like automatic caption generation and/or speech to textconversion can be used to extract the audio script from the scene, forexample, if captions are not available. The extracted segment includingtext and/or image can be transmitted to a classifier.

Target audience's profile can also be received or retrieved. In anembodiment, the profile can be associated with the multimedia. Forexample, the profile can be associated with the multimedia contentbefore the multimedia content is played by a user. As another example,the profile can be associated with the multimedia content at the timethe multimedia content is played. One or more profiles shown at 102 canbe received or retrieved.

At 106, for example, one or more classifiers are applied to the scriptand/or image of the segment to detect and flag a segment with potentialsensitive content. For instance, an image classifier or classificationalgorithm can be used to classify inappropriate image content on thescene. Similarly, a text classifier or classification algorithm can beused to classify inappropriate language or audio in the segment. Forexample, a classifier may output a score associated with the content,which score can indicate a degree of appropriateness orinappropriateness. As another example, the classifier may output a scoreindicating how close the content is for appropriateness corresponding tothe requested profile.

A segment determined to have inappropriate content can be flagged. Forexample, the segment can be tagged as having an inappropriate script(e.g., text, caption, audio), and/or inappropriate image and/or video.For instance, a segment content which has a score that exceeds a generalthreshold score can be flagged as being inappropriate.

At 108, a sensitivity analysis is conducted on the flagged segment toidentify if the segment is inappropriate given the audience profile. Forexample, a sensitivity analysis is conducted on the classificationresults. Segments of the media content can be tagged as sensitive ornon-sensitive based on the audience profile and the results ofclassification at 106. For example, the audience profile may indicate,for that particular audience, what type of content is consideredinappropriate. The classification at 106 can classify the type of theinappropriate content, for example, by score. Based on matching the typeof the inappropriate content and the audience profile indication, themedia content segment can be tagged as being sensitive. As anotherexample, the audience profile may indicate a threshold score for aninappropriate content for that particular audience. A classificationalgorithm or a classifier may output a score associated withinappropriate content. Based on the threshold score and the classifier'soutput score, a segment can be tagged sensitive or non-sensitive forthat audience. For example, if the score exceeds that audience'sthreshold score, the segment can be tagged as sensitive for thataudience.

At 110, if it is determined that the script of a segment is notappropriate for the given profile but the audio visual scene does notinclude sensitive content, the content of the media is altered for areplacement script for that segment. If the script of the segment isflagged as inappropriate, a new audience-appropriate script isgenerated. For example, a generative adversarial network (GAN) can beimplemented to generate new audience-appropriate script. By way ofexample, the generated text script of the sensitive content can beautomatically generated using AI technology (e.g., generative models,text generation) trained on historical data (e.g. movie scripts, novels,video games samples) and an analysis of context and events happening inthe scene. The generated audio of the sensitive content can beautomatically generated using AI technology (e.g., generative models,audio synthesis, face to speech technology). The generated imagery ofthe sensitive content can be automatically generated by using AItechnology (e.g., generative models, image synthesis).

The new content can be generated offline or in real time. Training ofthe model (e.g., the GAN model) can be done ahead of time. Once themodel or models are trained, the generation of the content can happen inreal time. In another embodiment, a variation of content for sensitivesegments can be automatically generated ahead of time and can beretrieved in real time as the media is streaming.

In an embodiment, for example, once a sensitive segment is up to play,before playing that scene, an alternate appropriate script can begenerated using generative models. The generated script or text can bepassed through the classifier to ensure it passes the sensitive score ofthe given audience profile. For example, there can be a feedback loop,where the generated script or text can be input to a classifier at 106and the sensitivity analysis at 108 performed again using the generatedscript.

An embodiment of an implementation can include replacing the offensiveor sensitive word with an accepted word. For example, responsive todetermining that a script contains a sensitive word X and in the scenean actor says that word, the system can alter the sensitive word X toanother word Y. If the segment contains a script or text, replacementscript or text can be provided (e.g., word Y). In addition, if thesegment involves imagery or video, the system can also recreate the lipmovement and facial features of the character as if the actor said theword Y. With a similar approach the audio is synthesized to the voice ofactor saying the word Y instead of X. Such processing can be implementedfor more than a word, for example, for a sentence. For example, analternative sentence can be generated and an entire original sentencecan be replaced.

For example, at 112, if the segment has audio, the generative audiosynthesis technology or another technique can be applied to generate theaudio for the given text.

At 114, if the segment also includes imagery, the system can alter thevisual content of the scene to reflect the new script. For example,using the deep learning technology, a video can be generated to mimicthe actors saying out the new script. Another example is using text toimage translation technology to create new imagery for the given script.

If it is determined that the script or text does not includeinappropriate content for the target audience, but contains visualcontent (e.g., imagery or video) which is classified as being sensitivefor that target audience, the system can generate a new imagery for thatscene. For example, if the actor is not dressed appropriately, theimagery of the scene can be updated using a deep learning technology,generative models, and/or text to image translation to fix the dressingof the actor. In an embodiment, once a sensitive segment is up to play,before playing that scene, an alternate appropriate imagery can begenerated, for example, using generative models and/or anothertechnique. For example, if the visual content is flagged as sensitivebut the audio script is clear (e.g., scenes that contains inappropriatevisual content but main characters speak of weather forecast), newvisual content can be generated for that segment based on an analysis ofthe audio or text script of the segment (for example, using text toimage translation), the context, story, and the main event in thatsegment.

At 118, the replaced or alternate content is played or presented on thedevice, or caused to be played or presented on the device. In anembodiment, a warning sign or signal can be shown indicating that theaudience is watching a synthetic video or listening to a syntheticaudio.

FIG. 2 is a flow diagram illustrating a method in an embodiment. At 202,the method includes receiving multimedia content to be played on amultimedia player device. A profile associated with a target audiencecan also be received.

At 204, the method includes determining that the multimedia contentcontains audience-inappropriate content. For example, the multimediacontent can be passed to a machine learning classifier such as anartificial neural network trained to classify content propriety. Theclassifier may output a score associated with appropriateness of thecontent. There can be a number of classifiers, e.g., an image or videoclassifier, text or script classifier, audio classifier. Each classifiermay process a different media of the multimedia content. Based on thescore output by a classifier and the target audience profile, the methodcan determine whether the multimedia content containsaudience-inappropriate content or content that is inappropriate for thattarget audience.

At 206, the method includes generating replacement content correspondingto the audience-inappropriate content. For instance, a machine learningor deep learning model such as a GAN model can be trained to generatethe replacement content. The replacement content is generated, whichwould be audience-appropriate. The replacement content can also bepassed through one or more classifiers to determine its appropriateness.

At 208, the method includes causing the generated replacement content toplay on the multimedia player device in lieu of theaudience-inappropriate content. The replacement content can be presentedwith a signal indicating that the replacement content is replacing anoriginal content.

For example, a text or script classifier may be run to determine thatthe multimedia content includes a script determined to be inappropriatefor a target audience, which script can be replaced with an alternativeor replacement script generated to be appropriate for the targetaudience. In addition, a video content corresponding to the replacementscript can also be generated, for example, the video content includingan actor speaking the replacement script, and/or with facial expressioncorresponding to the replacement script.

A video or image classifier can be run to determine that the multimediacontent includes a video content determined to be inappropriate for thetarget audience. In such a case, the replacement content can begenerated that includes a replacement video content.

By way of example, if audio and/or text script is flagged as sensitivecontent (audience-inappropriate content) but the visual content is clear(not audience-inappropriate), the audio and/or text script can bereplaced with a new script and the visual content can be synchronized tosupport that change, for example, facial expressions, lip movements.

In an embodiment, the replacement content can be automatically generatedusing artificial intelligence (AI) technology, e.g., generative models,text generation, audio synthesis, face to speech technology, and imagesynthesis, trained on historical data, e.g., movie scripts, novels,video games samples, celebrity images and audio samples, and an analysisof context and events happening in the scene.

In an embodiment, replacement audio content can be automaticallygenerated using AI technology, e.g., generative models, audio synthesis,face to speech technology. In an embodiment, replacement imagery can beautomatically generated by using AI technology, e.g., generative models,image synthesis.

In an embodiment, if the visual content is flagged as sensitive but theaudio script is clear, new visual content can be generated for thatsegment of the multimedia content based on an analysis of the audio ortext script of the segment, e.g., using text to image translation, thecontext, story, and the focus of the event in that segment.

Audience profiles can be categorized into different types or groups ofaudiences, and may be used to customize media content based on audiencepreferences such a clearance level, a replacement for confidentialinformation.

In an aspect, the method may dynamically replace the audio and videodepending upon the target audience settings and can be applicable forany type of streaming and/or multimedia files. Content such as text andvideo determined to be inappropriate for a target audience can bereplaced by a generated suitable text and corresponding video generatedvideo changes. The generated video and/or audio can vary with theselection of target audience, for example, based on the profile of thetarget audience.

Using facial recognition along with closed caption, the method can makethe multimedia match the desired spoken words based on viewer parameters(e.g., desired movie rating).

The following illustrates a use case example. A and B are parents to C.The family is watching their favorite movie. A and B have activated theaudience-appropriate content configuration on their playing device.There is an upcoming scene where the main character of the movie uses anoffensive language to talk to his colleague. The method in an embodimentflags that scene as containing sensitive script for C. A new script,video, imagery and closed caption can be generated to replace theoffensive language of that scene. The deep learning technologyregenerates the imagery of that scene to be synchronized with thegenerated spoken script. The alternative content gets played as part ofthat scene.

FIG. 3 is a diagram showing components of a system in one embodimentthat can generate audience-appropriate content. One or more hardwareprocessors 302 such as a central processing unit (CPU), a graphicprocess unit (GPU), and/or a Field Programmable Gate Array (FPGA), anapplication specific integrated circuit (ASIC), and/or anotherprocessor, may be coupled with a memory device 304, and generate aprediction model and recommend communication opportunities. A memorydevice 304 may include random access memory (RAM), read-only memory(ROM) or another memory device, and may store data and/or processorinstructions for implementing various functionalities associated withthe methods and/or systems described herein. One or more processors 302may execute computer instructions stored in memory 304 or received fromanother computer device or medium. A memory device 304 may, for example,store instructions and/or data for functioning of one or more hardwareprocessors 302, and may include an operating system and other program ofinstructions and/or data. One or more hardware processors 302 mayreceive multimedia content as input. Audience profile can also bereceived or retrieved. For instance, at least one hardware processor 302may train one or more machine learning classification models to classifyappropriateness of the multimedia content. The multimedia content can bepassed to such one or more classifiers. In one aspect, such multimediacontent and/or audience profile may be stored in a storage device 306 orreceived via a network interface 308 from a remote device, and may betemporarily loaded into a memory device 304. Another machine learningmodel such as, but not limited to, generative adversarial networks canbe trained and run to generate a replacement content to replace portionsor segments of the multimedia content determined to beaudience-inappropriate. One or more classifiers, generator models suchas GAN may be stored on a memory device 304, for example, for executionby one or more hardware processors 302. One or more hardware processors302 may be coupled with interface devices such as a network interface308 for communicating with remote systems, for example, via a network,and an input/output interface 310 for communicating with input and/oroutput devices such as a keyboard, mouse, display, and/or others.

FIG. 4 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment of the presentdisclosure. The computer system is only one example of a suitableprocessing system and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the methodologydescribed herein. The processing system shown may be operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with the processing system shown in FIG. 4 may include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 30 that performsthe methods described herein. The module 30 may be programmed into theintegrated circuits of the processor 12, or loaded from memory 16,storage device 18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. As used herein, the term “or” is an inclusive operator andcan mean “and/or”, unless the context explicitly or clearly indicatesotherwise. It will be further understood that the terms “comprise”,“comprises”, “comprising”, “include”, “includes”, “including”, and/or“having,” when used herein, can specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. As used herein, the phrase “in an embodiment” does notnecessarily refer to the same embodiment, although it may. As usedherein, the phrase “in one embodiment” does not necessarily refer to thesame embodiment, although it may. As used herein, the phrase “in anotherembodiment” does not necessarily refer to a different embodiment,although it may. Further, embodiments and/or components of embodimentscan be freely combined with each other unless they are mutuallyexclusive.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A computer-implemented method, comprising:receiving multimedia content to be played on a multimedia player device;determining that the multimedia content contains audience-inappropriatecontent by at least passing audio content of the multimedia content toan audio classifier, passing video content of the multimedia content toa video classifier; generating replacement content corresponding to theaudience-inappropriate content, wherein responsive to determining thatthe audio content is inappropriate based on the audio classifier's scoreand a target audience profile, an audio stream is generated asreplacement of the audio content and the generated replacement contentis iteratively passed to the audio classifier until the audioclassifier's score indicates appropriate content has been generated forthe target audience profile, wherein responsive to determining that thevideo content is inappropriate based on the video classifier's score andthe target audience profile, a visual content is generated asreplacement of the video content and the generated replacement isiteratively passed to the video classifier until the video classifier'sscore indicates appropriate video content has been generated for thetarget audience profile, wherein responsive to determining that theaudio content is appropriate but the video content is inappropriate, thegenerated visual content being generated based on text of the audiocontent to imagery translation; and causing the generated replacementcontent to play on the multimedia player device in lieu of theaudience-inappropriate content.
 2. The computer-implemented method ofclaim 1, wherein the method includes running an adversarial generativenetwork (GAN) to generate the replacement content.
 3. Thecomputer-implemented method of claim 1, wherein determining that themultimedia content contains audience-inappropriate content includesdetermining that the multimedia content includes a script determined tobe inappropriate for a target audience and replacing the script.
 4. Thecomputer-implemented method of claim 3, further including generating avideo content corresponding to the replacement script, the video contentincluding an actor's voice synthesized to speak the replacement script.5. The computer-implemented method of claim 1, wherein determining thatthe multimedia content contains audience-inappropriate content includesdetermining that the multimedia content includes a video contentdetermined to be inappropriate for a target audience, wherein thereplacement content generated includes a replacement video content. 6.The computer-implemented method of claim 1, wherein the generatedreplacement content is passed through a machine learning classifier, todetermine whether the generated replacement content isaudience-appropriate.
 7. The computer-implemented method of claim 1, thereplacement content is presented with a signal indicating that thereplacement content is replacing an original content.
 8. A systemcomprising: a hardware processor; and a memory device coupled with thehardware processor; the hardware processor configured to at least:receive multimedia content to be played on a multimedia player device;determine that the multimedia content contains audience-inappropriatecontent by at least passing audio content of the multimedia content toan audio classifier, passing video content of the multimedia content toa video classifier; generate replacement content corresponding to theaudience-inappropriate content, wherein responsive to determining thatthe audio content is inappropriate, an audio stream is generated asreplacement of the audio content and the generated replacement contentis iteratively passed to the audio classifier until the audioclassifier's score indicates appropriate content has been generated forthe target audience profile, wherein responsive to determining that thevideo content is inappropriate based on the video classifier's score andthe target audience profile, a visual content is generated asreplacement of the video content and the generated replacement isiteratively passed to the video classifier until the video classifier'sscore indicates appropriate video content has been generated for thetarget audience profile, wherein responsive to determining that theaudio content is appropriate but the video content is inappropriate, thegenerated visual content being generated based on the audio content'stext to image translation; and cause the generated replacement contentto play on the multimedia player device in lieu of theaudience-inappropriate content.
 9. The system of claim 8, wherein thehardware processor is configured to run an adversarial generativenetwork (GAN) to generate the replacement content.
 10. The system ofclaim 8, wherein the hardware processor is configured to determine thatthe multimedia content includes a script determined to be inappropriatefor a target audience and replace the script.
 11. The system of claim10, wherein the hardware processor is configured to generate a videocontent corresponding to the replacement script, the video contentincluding an actor's voice synthesized to speak the replacement script.12. The system of claim 8, wherein the hardware processor is configuredto determine that the multimedia content segment includes a videocontent determined to be inappropriate for a target audience, whereinthe replacement content generated includes a replacement video content.13. The system of claim 8, wherein the generated replacement content ispassed through a machine learning classifier, to determine whether thegenerated replacement content is audience-appropriate.
 14. The system ofclaim 8, wherein the replacement content is presented with a signalindicating that the replacement content is replacing an originalcontent.
 15. A computer program product comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a device to cause the device to:receive multimedia content to be played on a multimedia player device;determine that the multimedia content contains audience-inappropriatecontent by at least passing audio content of the multimedia content toan audio classifier, passing video content of the multimedia content toa video classifier; generate replacement content corresponding to theaudience-inappropriate content, wherein responsive to determining thatthe audio content is inappropriate, an audio stream is generated asreplacement of the audio content and the generated replacement contentis iteratively passed to the audio classifier until the audioclassifier's score indicates appropriate content has been generated forthe target audience profile, wherein responsive to determining that thevideo content is inappropriate based on the video classifier's score andthe target audience profile, a visual content is generated asreplacement of the video content and the generated replacement isiteratively passed to the video classifier until the video classifier'sscore indicates appropriate video content has been generated for thetarget audience profile, wherein responsive to determining that theaudio content is appropriate but the video content is inappropriate, thegenerated visual content being generated based on the audio content'stext to image translation; and cause the generated replacement contentto play on the multimedia player device in lieu of theaudience-inappropriate content.
 16. The computer program product ofclaim 15, wherein the device is caused to run an adversarial generativenetwork (GAN) to generate the replacement content.
 17. The computerprogram product of claim 15, wherein the device is caused to determinethat the multimedia content includes a script determined to beinappropriate for a target audience and replace the script.
 18. Thecomputer program product of claim 17, wherein the device is caused togenerate a video content corresponding to the replacement script, thevideo content including an actor's voice synthesized to speak thereplacement script.
 19. The computer program product of claim 15,wherein the device is caused to determine that the multimedia contentsegment includes a video content determined to be inappropriate for atarget audience, wherein the replacement content generated includes areplacement video content.
 20. The computer program product of claim 15,wherein the generated replacement content is passed through a machinelearning classifier, to determine whether the generated replacementcontent is audience-appropriate.